




'''simple docstring'''


import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest





@require_torchsde
class  __lowercase    (	__magic_name__			):
   _a    						=   (DPMSolverSDEScheduler,)
   _a    						=   10







   def 		UpperCamelCase__      (						self   ,				**UpperCamelCase   )					->     Optional[Any]:
     __a								=       {
         'num_train_timesteps': 1100,
         'beta_start': 0.0_001,
         'beta_end': 0.02,
         'beta_schedule': 'linear',
         'noise_sampler_seed': 0,
     }

     config.update(**UpperCamelCase   )
     return config







   def 		UpperCamelCase__      (						self   )					->     Optional[Any]:
     for timesteps in [10, 50, 100, 1000]:
       self.check_over_configs(num_train_timesteps=UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     str:
     for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]   ,				[0.0_002, 0.002, 0.02]   ):
       self.check_over_configs(beta_start=UpperCamelCase   ,				beta_end=UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Any:
     for schedule in ["linear", "scaled_linear"]:
       self.check_over_configs(beta_schedule=UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Optional[Any]:
     for prediction_type in ["epsilon", "v_prediction"]:
       self.check_over_configs(prediction_type=UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Optional[Any]:
     __a								=       self.scheduler_classes[0]
     __a								=       self.get_scheduler_config()
     __a								=       scheduler_class(**UpperCamelCase   )

     scheduler.set_timesteps(self.num_inference_steps   )

     __a								=       self.dummy_model()
     __a								=       self.dummy_sample_deter * scheduler.init_noise_sigma
     __a								=       sample.to(UpperCamelCase   )

     for i, t in enumerate(scheduler.timesteps   ):
       __a								=       scheduler.scale_model_input(UpperCamelCase   ,				UpperCamelCase   )

       __a								=       model(UpperCamelCase   ,				UpperCamelCase   )

       __a								=       scheduler.step(UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )
       __a								=       output.prev_sample

     __a								=       torch.sum(torch.abs(UpperCamelCase   )   )
     __a								=       torch.mean(torch.abs(UpperCamelCase   )   )

     if torch_device in ["mps"]:
       assert abs(result_sum.item() - 167.47_821_044_921_875   ) < 1e-2
       assert abs(result_mean.item() - 0.2_178_705_964_565_277   ) < 1e-3
     elif torch_device in ["cuda"]:
       assert abs(result_sum.item() - 171.59_352_111_816_406   ) < 1e-2
       assert abs(result_mean.item() - 0.22_342_906_892_299_652   ) < 1e-3
     else:
       assert abs(result_sum.item() - 162.52_383_422_851_562   ) < 1e-2
       assert abs(result_mean.item() - 0.211_619_570_851_326   ) < 1e-3







   def 		UpperCamelCase__      (						self   )					->     List[str]:
     __a								=       self.scheduler_classes[0]
     __a								=       self.get_scheduler_config(prediction_type='v_prediction'   )
     __a								=       scheduler_class(**UpperCamelCase   )

     scheduler.set_timesteps(self.num_inference_steps   )

     __a								=       self.dummy_model()
     __a								=       self.dummy_sample_deter * scheduler.init_noise_sigma
     __a								=       sample.to(UpperCamelCase   )

     for i, t in enumerate(scheduler.timesteps   ):
       __a								=       scheduler.scale_model_input(UpperCamelCase   ,				UpperCamelCase   )

       __a								=       model(UpperCamelCase   ,				UpperCamelCase   )

       __a								=       scheduler.step(UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )
       __a								=       output.prev_sample

     __a								=       torch.sum(torch.abs(UpperCamelCase   )   )
     __a								=       torch.mean(torch.abs(UpperCamelCase   )   )

     if torch_device in ["mps"]:
       assert abs(result_sum.item() - 124.77_149_200_439_453   ) < 1e-2
       assert abs(result_mean.item() - 0.16_226_289_014_816_284   ) < 1e-3
     elif torch_device in ["cuda"]:
       assert abs(result_sum.item() - 128.1_663_360_595_703   ) < 1e-2
       assert abs(result_mean.item() - 0.16_688_326_001_167_297   ) < 1e-3
     else:
       assert abs(result_sum.item() - 119.8_487_548_828_125   ) < 1e-2
       assert abs(result_mean.item() - 0.1_560_530_662_536_621   ) < 1e-3







   def 		UpperCamelCase__      (						self   )					->     List[Any]:
     __a								=       self.scheduler_classes[0]
     __a								=       self.get_scheduler_config()
     __a								=       scheduler_class(**UpperCamelCase   )

     scheduler.set_timesteps(self.num_inference_steps   ,				device=UpperCamelCase   )

     __a								=       self.dummy_model()
     __a								=       self.dummy_sample_deter.to(UpperCamelCase   ) * scheduler.init_noise_sigma

     for t in scheduler.timesteps:
       __a								=       scheduler.scale_model_input(UpperCamelCase   ,				UpperCamelCase   )

       __a								=       model(UpperCamelCase   ,				UpperCamelCase   )

       __a								=       scheduler.step(UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )
       __a								=       output.prev_sample

     __a								=       torch.sum(torch.abs(UpperCamelCase   )   )
     __a								=       torch.mean(torch.abs(UpperCamelCase   )   )

     if torch_device in ["mps"]:
       assert abs(result_sum.item() - 167.46_957_397_460_938   ) < 1e-2
       assert abs(result_mean.item() - 0.21_805_934_607_982_635   ) < 1e-3
     elif torch_device in ["cuda"]:
       assert abs(result_sum.item() - 171.59_353_637_695_312   ) < 1e-2
       assert abs(result_mean.item() - 0.22_342_908_382_415_771   ) < 1e-3
     else:
       assert abs(result_sum.item() - 162.52_383_422_851_562   ) < 1e-2
       assert abs(result_mean.item() - 0.211_619_570_851_326   ) < 1e-3







   def 		UpperCamelCase__      (						self   )					->     int:
     __a								=       self.scheduler_classes[0]
     __a								=       self.get_scheduler_config()
     __a								=       scheduler_class(**UpperCamelCase   ,				use_karras_sigmas=UpperCamelCase   )

     scheduler.set_timesteps(self.num_inference_steps   ,				device=UpperCamelCase   )

     __a								=       self.dummy_model()
     __a								=       self.dummy_sample_deter.to(UpperCamelCase   ) * scheduler.init_noise_sigma
     __a								=       sample.to(UpperCamelCase   )

     for t in scheduler.timesteps:
       __a								=       scheduler.scale_model_input(UpperCamelCase   ,				UpperCamelCase   )

       __a								=       model(UpperCamelCase   ,				UpperCamelCase   )

       __a								=       scheduler.step(UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )
       __a								=       output.prev_sample

     __a								=       torch.sum(torch.abs(UpperCamelCase   )   )
     __a								=       torch.mean(torch.abs(UpperCamelCase   )   )

     if torch_device in ["mps"]:
       assert abs(result_sum.item() - 176.66_974_135_742_188   ) < 1e-2
       assert abs(result_mean.item() - 0.23_003_872_730_981_811   ) < 1e-2
     elif torch_device in ["cuda"]:
       assert abs(result_sum.item() - 177.63_653_564_453_125   ) < 1e-2
       assert abs(result_mean.item() - 0.23_003_872_730_981_811   ) < 1e-2
     else:
       assert abs(result_sum.item() - 170.3_135_223_388_672   ) < 1e-2
       assert abs(result_mean.item() - 0.23_003_872_730_981_811   ) < 1e-2









'''simple docstring'''


from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


UpperCAmelCase_					=   logging.get_logger(__name__)

UpperCAmelCase_					=   {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

UpperCAmelCase_					=   {
    "vocab_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"
    },
    "merges_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"
    },
    "tokenizer_config_file": {
        "facebook/blenderbot_small-90M": (
            "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"
        )
    },
}

UpperCAmelCase_					=   {
    "facebook/blenderbot_small-90M": 5_12,
}





class  __lowercase    (	__magic_name__			):
   _a    						=   VOCAB_FILES_NAMES
   _a    						=   PRETRAINED_VOCAB_FILES_MAP
   _a    						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _a    						=   BlenderbotSmallTokenizer







   def __init__(						self   ,				UpperCamelCase=None   ,				UpperCamelCase=None   ,				UpperCamelCase="<|endoftext|>"   ,				UpperCamelCase="<|endoftext|>"   ,				UpperCamelCase="<|endoftext|>"   ,				UpperCamelCase=False   ,				UpperCamelCase=True   ,				**UpperCamelCase   ,				)					->     List[Any]:
     super().__init__(
         ByteLevelBPETokenizer(
             vocab=UpperCamelCase   ,				merges=UpperCamelCase   ,				add_prefix_space=UpperCamelCase   ,				trim_offsets=UpperCamelCase   ,				)   ,				bos_token=UpperCamelCase   ,				eos_token=UpperCamelCase   ,				unk_token=UpperCamelCase   ,				**UpperCamelCase   ,				)
     __a								=       add_prefix_space







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase=None   )					->     List[Any]:
     __a								=       [self.bos_token_id] + token_ids_a + [self.eos_token_id]
     if token_ids_a is None:
       return output

     return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase = None   )					->     List[int]:
     __a								=       [self.sep_token_id]
     __a								=       [self.cls_token_id]

     if token_ids_a is None:
       return len(cls + token_ids_a + sep   ) * [0]
     return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]



